RX 7900 XT 20GBGünstige Wahl
20 GB VRAM (+4)800 GB/s (+224)
A
Adds memory headroom for longer context windows and future model growth.98 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $899 MSRP
Can Baichuan 7B run on Radeon RX 7900M 16GB?
YES — Tight Fit
B70Good
Estimated from fit model
Baichuan 7B needs ~14.6 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~80 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 14.6 GB, 79.6 tok/s, Tight fit
14.6 GB required16.0 GB available
91% VRAM used
Fit status
Tight fit
Decode
79.6 tok/s
TTFT
2433 ms
Safe context
8K
Memory
14.6 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsBaichuan 7B on Radeon RX 7900M 16GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 79.6 tok/s decode · 2.4s TTFT (warm) · 199 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 79.6 tok/s | 1327 ms | 8K |
| Coding | B | Tight fit | 79.6 tok/s | 2433 ms | 8K |
| Agentic Coding | F | Too heavy | 29.4 tok/s | 9575 ms | 8K |
| Reasoning | B | Tight fit | 79.6 tok/s | 2875 ms | 8K |
| RAG | F | Too heavy | 29.4 tok/s | 11968 ms | 8K |
Quantization options
How Baichuan 7B (7B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B63 |
Q3_K_S | 3 | 3.4 GB | Low | B63 |
NVFP4 | 4 | 3.9 GB | Medium | B64 |
Q4_K_M | 4 | 4.3 GB | Medium | B64 |
Q5_K_M | 5 | 5.0 GB | High | B65 |
Q6_K | 6 | 5.7 GB | High | B66 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B67 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run Baichuan 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "baichuan-inc/Baichuan-7B" \
--hf-file "Baichuan-7B-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade-Optionen
Hardware, die Baichuan 7B gut ausführt
Frequently asked questions
Can Radeon RX 7900M 16GB run Baichuan 7B?
Yes, Radeon RX 7900M 16GB can run Baichuan 7B with a B grade (Tight fit). Expected decode speed: 79.6 tok/s.
How much VRAM does Baichuan 7B need?
Baichuan 7B (7B parameters) requires approximately 14.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Baichuan 7B?
The recommended quantization for Baichuan 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will Baichuan 7B run at on Radeon RX 7900M 16GB?
On Radeon RX 7900M 16GB, Baichuan 7B achieves approximately 79.6 tokens per second decode speed with a time-to-first-token of 2433ms using Q4_K_M quantization.
Can Radeon RX 7900M 16GB run Baichuan 7B for coding?
For coding workloads, Baichuan 7B on Radeon RX 7900M 16GB receives a B grade with 79.6 tok/s and 8K context.
What context window can Baichuan 7B use on Radeon RX 7900M 16GB?
On Radeon RX 7900M 16GB, Baichuan 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/baichuan-7b-on-rx-7900m-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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